EP2261626A2 - Procédé et dispositif d'équilibrage - Google Patents

Procédé et dispositif d'équilibrage Download PDF

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Publication number
EP2261626A2
EP2261626A2 EP10178219A EP10178219A EP2261626A2 EP 2261626 A2 EP2261626 A2 EP 2261626A2 EP 10178219 A EP10178219 A EP 10178219A EP 10178219 A EP10178219 A EP 10178219A EP 2261626 A2 EP2261626 A2 EP 2261626A2
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Prior art keywords
control
vibration
value
balance
influence coefficient
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German (de)
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EP2261626A3 (fr
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Stephen William Dyer
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Lord Corp
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Lord Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M1/00—Testing static or dynamic balance of machines or structures
    • G01M1/30—Compensating imbalance
    • G01M1/36—Compensating imbalance by adjusting position of masses built-in the body to be tested
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M1/00—Testing static or dynamic balance of machines or structures
    • G01M1/14—Determining imbalance
    • G01M1/16—Determining imbalance by oscillating or rotating the body to be tested
    • G01M1/22—Determining imbalance by oscillating or rotating the body to be tested and converting vibrations due to imbalance into electric variables

Definitions

  • This invention generally relates to a method and an apparatus for balancing and more particularly to a method and to an apparatus for balancing a rotating machine tool and/or rotating/moving machine tool assembly.
  • Machine tool assemblies are used to create and/or to form various parts, products, and other types of entities and/or apparatuses.
  • these apparatuses include tools which are usually and removably placed, by means of a tool holder, upon a rotating or moving spindle and which are adapted to engage a workpiece or other apparatus and to engagingly form the workpiece into a desired shape and size.
  • these machine tool assemblies adequately create the desired parts, products, and/or other entities they are prone to imbalance failure and/or imbalance error.
  • the precision and reliability of these relatively high-speed machine tool assemblies is limited by the imbalance which exists and/or arises as the assembly is used (e.g. as the tool is engaging rotated and/or moved against the workpiece).
  • Applicant's invention addresses these needs and these aforedescribed drawbacks of these prior art balancing devices and methodologies in a new and novel fashion.
  • Applicant's invention is not limited to the balancing of a machine tool assembly. Rather, Applicant's invention is directed to the balance of any moving and/or rotating member.
  • a balancer is provided.
  • the balancer includes a balancer to selectively balance unbalance in a rotating tool assembly; and control means, connected to the balancer, for causing said balancer to calculate an influence coefficient of the rotating tool assembly and to thereafter selectively balance the rotating tool assembly by the movement of the balancer, movement being defined by said calculated influence coefficient.
  • the balancer includes a balancing means to selectively balance a rotating tool assembly; and control means, coupled to said balancing means, for dynamically calculating vibration levels necessary for said balancing to begin and to end.
  • a balancer methodology includes the steps of a method of balancing a tool assembly by selectively employing a balance weight correction to said tool assembly, said method comprising the steps of employing a first balance weight correction to said tool assembly, measuring an amount of vibration associated with said balance tool assembly, estimating an influence coefficient value of said tool assembly, dividing said measured amount of vibration by said estimate influence coefficient thereby creating a certain value; multiplying said certain value by a gain parameter value, thereby creating a second certain value and subtracting said second certain value from said first balance weight correction, thereby creating a new balance weight correction and applying said new balance weight connection to said tool assembly.
  • a balancer is provided which substantially reduces and/or eliminates transitory vibration increases.
  • Figure 1 is a block diagram of a balancing apparatus made and operating in accordance with the preferred embodiment of the invention and shown operatively coupled to and deployed upon a typical tool assembly.
  • FIG. 1 there is shown a balancing assembly 10 shown in operative and assembled relation with a typical and conventional tool assembly 12. Assembly 10, as shown, is made in accordance and operates in accordance with the preferred embodiment of the invention.
  • tool assembly 12 includes a moving or selectively rotatable spindle 14 having an integral tool holder 16 into which a tool 18 is removably placed.
  • the movement and/or rotation of spindle 14 causes the tool 18 to move and/or rotate and to engage a workpiece 20 for the purpose of "machining" the workpiece 20 into some sort of desirable shape, size, and/or geometry.
  • tool assembly 12 is subject to unbalances which cause the tool 18 to perform imprecisely and undesirably and which causes the tool 18 and/or other portions of assembly 12 to fail and/or to become fatigued.
  • balancer assembly 10 includes a controller 22 which in one embodiment of the invention comprises a microprocessor acting and/or operating under stored program control and an electrical driver 24 which is selectively coupled to the source of electrical power 26 through controller 22 and which is adapted to selectively couple electrical power to the balancer actuators and/or rotors 28, 30 in a manner effective to correct a measured and/or calculated unbalance condition associated with the tool assembly 12, by movement of these rotors 28, 30.
  • Such unbalance measurement may be made by the use of one or more vibration sensors 32.
  • balancer assembly 10 further includes a position sensor 34 which is communicatively and operatively coupled to the controller 22 and which is adapted to provide positioning information associated with the placement and/or positioning of the rotors 28, 30, to the controller 22.
  • Applicant's invention is directed to the manner of moving these unbalance correction rotors or other balance correction members.
  • Applicant's invention may be used with a wide variety of computerized balancing systems; each of these systems being adapted to selectively provide a balancing weight correction achieved by the movement of a certain member or members each having a certain non-symmetrical or uneven mass distribution.
  • a balancing system 10 is shown for example and without limitation, within United States Patent Number 5,757,662; which was filed on November 29, 1994 ; which issued on May 26, 1998; which is assigned to Applicant's assignee; and which is fully and completely incorporated herein by reference, word for word and paragraph for paragraph.
  • Another non-limiting example of a balancing system 10 is shown within United States Patent Number 4,977,510; which was filed on July 21, 1989 ; which issued on December 11, 1990; which is assigned to Applicant's assignee; and which is fully and completely incorporated herein by reference, word for word and paragraph for paragraph.
  • an automatic and/or substantially automatic tuning mechanism is provided which is based upon two adaptive parameters which are calculated during each control iteration based upon the convergence of the influence coefficient estimate. These parameters are used to define the correction movement of the balancer 10 and/or of those movable balancer members of the balancer assemblies in order to balance a tool assembly spindle, turbomachinery, or other moving member.
  • the measured vibration of the tool assembly 12 can be considered to be an "error signal" that is desired to be eliminated by the use of an unbalance compensation apparatus, such as and without limitation balancer assembly 10.
  • vibrational data is sampled and depending upon the "level” or amount of reported unbalance, the rotors 28, 30 are moved to a certain position. That is, vibrational data emanating from the sensor(s) 32 is communicatively coupled to the balancer controller 22 where it is "recognized” and where it causes certain balance operations to occur in order to minimize the amount of unbalance present within the tool assembly 12.
  • This "error signal” can be mathematically represented as a complex phasor having a certain phase angle (e.g. obtained by the use of a fixed target on rotating assembly which is utilized by the position sensor 34) and a magnitude.
  • the total vibrational error signal- can be thought of as the linear and mathematical summation of all of the system "disturbance” (e.g. anything of a physical or electrical nature that might cause the tool system 12 to become unbalanced, such as and without limitation a "wearing away” of a portion of the tool holder) and of the overall effect of the balance corrections which were made to the system.
  • disurbance e.g. anything of a physical or electrical nature that might cause the tool system 12 to become unbalanced, such as and without limitation a "wearing away” of a portion of the tool holder
  • the vibrational error associated with and/or "reported”/calculated/measured at of each control iteration is the mathematical sum of all of the disturbances present during or at that particular iteration and the mathematical multiplicative product of the balance correction made during that interval with the influence coefficient of the machine.
  • the influence coefficient "C( ⁇ )” is a measure of the total response of assembly 12 to a certain amount of unbalance correction and is calculated and/or estimated in measurement units of units of vibration per unit of unbalance.
  • the metric "C( ⁇ )" defines and/or is a measure of the response of the system 12 to a certain amount of applied unbalance compensation and is a transfer function.
  • the value of this influence coefficient may change.
  • This gain parameter ⁇ can improve control robustness to errors in the influence estimate ⁇ .
  • We can, without loss of generality, consider the disturbance d to be represented by the influence of a hypothetical unbalance u (which may include any synchronous forcing, not just unbalance) acting through some cumulative influence coefficient c u such that d c ⁇ c u ⁇ u
  • This criterion can be represented graphically in the complex plane as shown in Figure 2 .
  • ⁇ is defined to be greater than zero, if the phase angle of the influence coefficient estimate lies between ⁇ 90° (noninclusive) of the actual influence coefficient phase angle (i.e., the real part of the quotient c / ⁇ is greater than zero), then there exists a gain ⁇ for which the control is stable.
  • the forgetting factor ⁇ can be chosen to provide an optimum trade-off between estimation sensitivity to measurement noise, and speed of estimation convergence.
  • the estimation variance can be reduced by selecting a low value for ⁇ .
  • the estimation variance approaches the variance of the calculated parameter c new . This relationship is shown in Figure 3a .
  • the estimation will "track" the actual influence coefficient c as it varies over time.
  • the disadvantage of choosing a low forgetting factor ⁇ is that estimation convergence rate is reduced.
  • the number of estimation iteration required for 95% convergence of the estimate for varying ⁇ values is plotted in Figure 3b .
  • the supervisory strategy used for these experiments was to "turn off" the estimation whenever the change in control vectors w k - w k-1 fell below some predetermined low level. This level was chosen base on the balance correction resolution and measurement noise of the balance correction vector.
  • the control was deactivated whenever the vibration error level fell below a preset limit. When deactivated, the controller could continue to monitor the vibration error and reactivate the control and estimation when the error exceeded a certain limit.
  • the limit level included some "hysteresis" to prevent annoying on-off cycles of the controller.
  • the vibration error limit at which the controller was somewhat higher than the limit at which the controller turned off.
  • a flow chart of this supervisory strategy is shown in Figure 5 .
  • Such a supervisory control strategy is important for machine tool applications as active balance corrections should only be performed prior to, not during, metal cutting.
  • the balancing system was configured so that it could receive an "activate" command, and after successful control of vibration error below the limit level, would deactivate itself. Vibration error, would be monitored continuously for history logging purposes.
  • Test spindle 1 was a 23,700 rpm, 1kW air-cooled spindle.
  • Test spindle 2 was a 15,000 rpm, 75kW liquid-cooled spindle.
  • a Hall Effect sensor detected a fixed magnetic target on the rotating shaft to compute spindle rotational speed and to act as a phase reference.
  • the synchronous vibration error phasor was computed each control iteration as discussed previously. Thirty-two (32) samples of spindle housing radial vibration (measured with a piezoelectric accelerometer) were obtained every spindle revolution with sampling synchronized to spindle rotation. Data from multiple spindle revolutions (typically six) were then time-synchronous averaged to provide additional filtering of noise and non-synchronous components.
  • Balance correction was measured by detecting the passing of a magnet target on each balance rotor by stationary Hall Effect sensors. The phase lag of these events compared to the shaft phase reference target was calculated to determine the angular location of each balance rotor.
  • Test spindle 1 was used to test the adaptive control system robustness in the presence of nonlinear dynamics.
  • the spindle exhibited a "hardening" stiffness effect when vibrating at large amplitudes.
  • the spindle dynamic stiffness was about two times greater at high unbalance levels than at low unbalance levels. For example, on unit of unbalance would cause on unit of vibration, but two units of unbalance would result in only about 1.5 units of vibration.
  • a non-adaptive linear influence coefficient based control algorithm generally may have difficulty quickly achieving low vibration in the presence of such a harsh nonlinearity. Control stability may also be in question depending on the initial conditions. For the test described here, an initial estimate of the influence coefficient magnitude and phase in the range of previously measured varying values was supplied to the controller.
  • Figure 6 shows how the measured spindle housing vibration at the operating speed 10,000 rpm was reduced to an acceptable level by the adaptive controller.
  • Figure 6 shows that vibration was controlled to below the low limit in two balance correction iterations the time required for the vibration data sampling, estimation, and control computation is evident by noting the time between the end of the first balance iteration and the start of the second (observable when the vibration again begins to decrease again just before one second). A portion of this time between balance weight positioning was allotted as a fixed delay to allow the transient vibration to settle.
  • the erroneous initial influence coefficient estimate caused the controller to move the balance weights in the "wrong" direction, causing undesired temporary increase in vibration which could have permanently harmed the tool assembly.
  • new input-output data was utilized in a weighted average to re-estimate the system unbalance response vector.
  • the active balancing system controlled vibration to below the preset low limit. The controller then had acquired, or "learned", the spindle dynamic response at that operating speed during the exercising of control.
  • the active balancing system can be used as a probe to measure and track the inherent dynamic response of a system.
  • Such data contains useful diagnostic information such as and without limitation data associated with the dynamic bearing stiffness and effective rotating inertia.
  • test results shown in Figure 9 indicate how quickly the single-plane control system can respond when an accurate estimate of the influence coefficient vector is available.
  • test spindle 2 resulted in significantly decreased vibration at all speeds.
  • the spindle was run at various constant speeds from 5,000 rpm up to its maximum operating speed of 15,000 rpm and active balancing at each speed and is shown in Figure 10 .
  • the spindle manufacturer considered 2 mm/sec to be acceptable but less than 1 mm/sec vibration desirable to protect spindle bearing life.
  • the adaptive active balancing system reduced synchronous vibration by 79-99%.
  • the control system was able to maintain synchronous vibration at or below 0.5 mm/sec even when initial vibration well exceeded acceptable levels.
  • Background vibration "noise" and the positioning resolution of the balance mass actuators determined the minimum achievable controlled vibration level.
  • the amount the balance correction magnitude and phase had to change over the speed range is a measure of how "upstream", or co-located, the vibration control was. If the optimal balance correction remained the same over the entire speed range, we would know that the balance correction was exciting the same vibrational modes as the disturbance unbalance. In this case the control input would be entering the system in the same way as the disturbance and the control could be considered to be eliminating the source of harmful vibration. If the balance correction were required to change significantly over the speed range it would be evidence of more "downstream” or non-colocated control. That is, the control would be exciting different vibrational modes than the disturbance unbalance. The control, therefore, could only eliminate the effect of the disturbance at the sensor, not the disturbance itself.
  • a significantly useful side benefit of the active balancing system is that it can be used as a probe to measure and track the unbalance response of a system.
  • Such system identification information contains useful data about the dynamic bearing stiffness and effective rotating mass.
  • the unbalance sensitivity was stored at each speed every time the active balancing system was enabled to control vibration. This data was obtained for two spindle system configurations to allow for comparisons. A long tool and short tool were separately inserted into the spindle and active balancing control enabled for successive rotational speeds.
  • Figure 12 and Figure 13 below show the measured unbalance sensitivity amplitude and phase angle for the two tool configurations.
  • the single plane adaptive influence coefficient recursive control law was earlier derived.
  • the controller 10 commands the balance correction at control iteration k +1 to the state given in Eq. (3.17) by the use of " ⁇ " and " ⁇ " control parameters. The manner in which this is accomplished is further shown below.
  • the overall adaptively controlled error response can be derived.
  • Such a scenario is representative of the typical machine tool active balancing application. After each tool change, balancing is performed at the new operating speed at which cutting is to be performed.
  • the relevant dynamics can, in general, be expected to vary significantly from tool change to tool change because of different spindle operating speeds, toolholder inertias, and geometric configurations of the machine structure. However once the spindle is accelerated to the new operating speed, the unbalance disturbance and machine dynamics do not typically change during the few seconds in which active balancing is performed.
  • the error magnitude will begin to decrease.
  • the magnitude of the error at the p th iteration depends on the magnitude of the estimation error ( ⁇ 0 - c ) and the value of ⁇ . An example of this is shown in Figure 14 .
  • the stability analysis above is based on the assumption that neither the unbalance disturbance nor the machine dynamics changed during control convergence. However, it is reasonable to extend the stability conclusion to certain cases of time-varying disturbances and influence coefficients. Simplistically speaking, after initial convergence, the on-line estimation would "track" changes in the machine dynamics with exponential convergence as long as the influence coefficient varied less than 2 ⁇ times the previous influence coefficient each control iteration. This is because the estimation will respond a fraction ⁇ times the distance to the actual value during each iteration. Similarly, if the unbalance disturbance changes less than 2 ⁇ times the previous value during each iteration, the control would still exhibit exponential convergence because the control would get closer to the optimal value each iteration.
  • a k and ⁇ k are the control gain and estimation forgetting factors respectively to be used at each control iteration k . by the controller 22 and where ⁇ ⁇ and ⁇ ⁇ are arbitrary real scaling factors to allow flexibility in shaping the response of each parameter.
  • Eq. (4.7) and Eq. (4.8) ensure that when the influence estimation error is high, the control gain ⁇ k is low. Examples of acceptable scaling factors include the value of 1 for each factor.
  • a supervisory control strategy is necessary for many machine tool applications because active balance corrections should only be performed prior to, not during, metal cutting.
  • the balancing system should be configured so that it could receive an "activate” command and, after successful balancing, would deactivate itself and signal the machine controller to begin machining. If the balancing system were not deactivated, the nonstationary synchronous vibration signal likely to be present during machining could lead to the active balancing device constantly "hunting" to find the optimal balance correction. Balance adjustments based on the vibration during cutting could potentially cause an increase in unbalance and harmful vibration. Furthermore, such continuous operation requires more power to the balance actuators and could lead to undo wear and tear on the device. To alleviate these problems, a supervisory strategy is in order to prevent excessive balance adjustments and estimation bursting.
  • a practical consideration for end users of such an active balancing system is how to set the vibration error limits to activate and deactivate control appropriately.
  • measurement noise, variation in machine dynamics and resolution limitations of the active balancing device constrain just how low the vibration error can be controlled.
  • some specialized engineering knowledge must be applied in selecting supervisory limit vales. Such specialized knowledge is not always available to the typical end-user of the active balancing system.
  • the limit levels would require readjustment. Therefore, an automated method of selecting control limits is proposed here that incorporates the considerations mentioned above.
  • the worst case correction resolution can be defined.
  • This resolution in combination with the estimate of the system influence coefficient, can be used to define the low vibration error limit at which control will "deactivate".
  • ⁇ k is the estimated influence coefficient a the current control iteration k
  • w res is the worst case balance correction resolution (in the same unbalance units used in the influence coefficient)
  • ⁇ e is a unit-less scaling factor (simply for added flexibility in shaping response)
  • ⁇ k is the influence coefficient estimation error defined in Eq. (4.6).
  • the exponential term is included to ensure that the control does not prematurely deactivate because of an erroneous influence coefficient estimate.
  • the influence coefficient estimation is converged ( ⁇ k is small) the low control limit will be the vibration error expected at the worst case resolution.
  • the low limit of Eq. (4.11) was chosen so that, in the worst case, the mean value of vibration error magnitude will be exactly at the low limit. In the most cases, the mean error magnitude will be less than the low limit.
  • the measured error magnitude will fluctuate about the mean value because of measurement noise. Assuming that the measurement noise is Gaussian, even in the worst case when the error magnitude mean were stationary at the low limit, there would be a 99.7% probability that the measured error magnitude would not exceed the high limit of Eq. (4.12). therefore, by automatically defining the high limit this way, there is a high probability that control would not be spuriously reactivated purely because of measurement noise.
  • Eq. (4.11) and Eq. (4.12) to automatically select supervisory control limits, no specialized user knowledge or user input is required. Furthermore, these automatic selection criteria take into account specific plant dynamics, active balance correction resolution and vibration error measurement noise.
  • An active balancing device was mounted on a conventional and commercially available high-speed grinding spindle and the auto-tuning adaptive control tested for various cases of unknown step changes in the unbalance disturbance and influence coefficient.
  • test spindle was a Fischer Model No. MFW-1240 grease-lubricated liquid cooled 10 kW high- speed grinding spindle. This would be a typical state-of-the-art spindle for CBN grinding applications.
  • the spindle allowed for safe testing at the relatively high rotational speed of 20, 000 rpm.
  • Figure 17 shows the comparative results for the condition of an erroneous initial estimate of the influence coefficient.
  • the initial influence estimate used was approximately 180° out of phase with the actual influence coefficient.
  • Figure 21 shows the comparative performance of the auto-tuning and conventional adaptive control when an accurate influence estimate is available.
  • Figure 22 shows the corresponding filtered synchronous vibration error values and adaptive parameters during the control convergence.
  • the auto-tuning control performs just as well as the conventional adaptive control.
  • the only situation in which the auto-tuning control will not perform at least as well as the conventional control is when the conventional control can converge in one step. Since a low initial value of control gain ⁇ k is used in the auto-tuning control, the control will never converge in only one step. It would often be able to converge in two steps in the same conditions, however.
  • the adaptive parameter auto-tuning functions introduced above enhance conventional adaptive influence control by limiting the worst case temporary vibration error while allowing the same (or better) control convergence rate. Furthermore, because parameters are varied automatically during each control iteration, no user setup is required. This is typically a distinct advantage because no special operator training is required to setup and use the vibration control system for each individual machine or environmental condition.
  • the automatic supervisory limit selection method was enabled during the experimental testing.
  • Figure 23 shows the automatically calculated supervisory limit settings from the experiment shown in Figure 18 .
  • Control was deactivated once vibration error magnitude was controlled below the low limit at about 1.0 seconds elapsed time. Control was never reactivated because vibration error magnitude never exceeded the high limit. The standard deviation of the vibration error magnitude measurement noise during this test was 0.0029 g's.
  • the active balancing device used in the test had two stepper-motor type balance rotors each with 60 detent increments per revolution. The worst case balance correction resolution for this configuration is ⁇ / n det ents (i. e., 5.2%) of the maximum balance correction capacity of the device.
  • Applicant's invention substantially eliminates performance trade-offs inherent in the fixed-parameter adaptive control and automatically incorporates engineering knowledge to simplify the active balancing system operation for the end-user.
  • the adaptive influence coefficient method allows active balancing control without a priori modeling or experimentation to determine plant dynamics.
  • a stability analysis was presented for the fixed-parameter adaptive control in the typical case where the synchronous disturbance and plant influence coefficient control was shown to be stable in these cases regardless of the initial influence coefficient estimate.
  • the worst case temporary vibration during control adaptation could become quite large for erroneous influence coefficient estimates.
  • a trade-off exists between speed of control convergence and the magnitude of this worst case error.
  • An automatic tuning method was therefore presented that allows the adaptive control parameters to be adjusted each control iteration to limit the low worst case error magnitude while still providing fast control convergence.
  • the parameters were adjusted each control iteration based on an instantaneous measure of the influence coefficient estimation accuracy. When estimation was inaccurate, the control become less aggressive and the estimation placed more weight on the instantaneously measured influence coefficient. When estimation error was low, the adaptive parameters were adjusted to provide more aggressive control and to place more weight on the long-term averaged estimation.
  • Supervisory control is necessary to "turn off” control so that machining operations can proceed once vibration is controlled below an acceptable limit.
  • Significant specialized engineering knowledge is typically required to set supervisory vibration error limits for each application.
  • Automatic supervisory limit selection criteria were defined to eliminate the need for such specialized end user input.
  • the vibration error limits for enabling and disabling control were defined based on functions of estimate of machine dynamics, estimation accuracy, active balance mass actuator resolution and vibration measurement noise.
  • the influence matrix is not square. Because the influence matrix does not exist in this case, the control law of Eq. (5.1) cannot be used. Active balancing, therefore, cannot in general drive all the error signals to zero.
  • researchers involved in off-line balancing have noted that least squares solution (or "pseudo-inverse") of influence coefficient balancing can be implemented for non-square influence matrices.
  • Such a control scheme allows the minimization of the sum of the squares of error signal residuals. In the event that not every error sensor output were deemed as important as every other, a weighted least squares control law can be implemented.
  • Similar real positive semi-definite m x m (where m is the number of active balance planes) weighting matrices [ R ] and [ S ] can be defined to penalize control effort, and control rate of change respectively.
  • the optimal control problem then consists of commanding the balance weight vector for the next control iteration ⁇ W ⁇ k+1 so as to minimize the objective function J .
  • the [ R ] matrix allows penalizing the control effort at each active balance plane, which could be desirable in certain cases. For example, if the source of residual unbalance were not co-located with the active balance planes, the optimal balance correction could be different for different operational speeds. The optimal balance correction at operating speed may sometimes cause harmful vibration at another slower speed if an emergency shutdown resulted in deceleration through a critical speed faster than the active balancing control could track. By conservatively limiting the control effort using a nonzero [ R ] matrix, higher vibration levels at operating speed could be traded off for a reduced possibility of causing harmful vibration during an emergency shutdown of the rotating machine.
  • the [ S ] matrix provides for penalizing the speed of control response. This can have the benefit of potentially allowing enough time for operator intervention in the case of any sort of malfunction of the active balancing system.
  • both the [ R ] and [ S ] matrices provide opportunities for cautious control that is often required in very conservative industries utilizing turbomachinery in critical continuous processes. Furthermore, both matrices can have the added benefit of enhancing the stability margin of non-adaptive control. By limiting the control inputs or slowing down the control response, an implementation that might originally be unstable due to a bad influence matrix estimate can be made stable. An analysis of the optimal control stability follows.
  • the control input ⁇ W ⁇ k +1 must converge to a constant value. Only the first term multiplied by ⁇ W ⁇ k is germane to the stability question. The second term multiplying ⁇ D ⁇ is constant and only affects the control vector to which the steady-state control converges. It then follows that the optimal active balancing control is stable for a constant disturbance vector ⁇ D ⁇ and constant influence matrices [ C ] and [ ⁇ ] if and only if ⁇ ⁇ C ⁇ * Q ⁇ C ⁇ + R + S - 1 ⁇ C ⁇ * Q ⁇ C ⁇ - C + S ⁇ 1 where the symbol ⁇ signifies the maximum singular value.
  • the adaptive control developed in Chapter 3 can be extended for multiple-plane applications.
  • the multiple-plane estimation also utilizes the current and most recent previous sets of measurements to calculate the instantaneous value of the influence matrix. This instantaneous value is then recursively averaged with the previous estimate.
  • the [ ⁇ W ] matrix must be at least ( m x m ) and non-singular to compute [ C ] new .
  • measured data corresponding to ( m +1) independent balance correction states must be obtained to re-calculate the influence coefficient matrix.
  • [ I mxm ] indicates the m x m identity matrix and ⁇ T ⁇ p indicates the p th column of the basis matrix [ T ].
  • the matrix [T] is exactly the identity matrix.
  • each sub-iteration would involve moving one balance plane correction at a time for maximum correction vector independence and maximum influence matrix estimation signal-to-noise ratio.
  • each sub-iteration would involve moving each balance correction plane simultaneously the same amount. This would minimize the worst temporary error signal amplitude (assuming an accurate influence matrix). This would also, however, result in a singular [ ⁇ W ] matrix and render estimation of the influence matrix impossible.
  • the ⁇ parameter can be specified to provide the desired trade-off between multiple plane influence coefficient estimation signal-to-noise ratio and worst-case error signal amplitude during active balancing.
  • a recursive adaptive implementation of the optimal control law of Eq. (5.8) can be obtained by the addition of the on-line estimation given in Eq. (5.20) and the "dithering" method governed by the basis matrix of Eq. (5.21).
  • [ ⁇ ] k is the estimated influence matrix computed using Eq. (5.20).
  • Each control iteration k is divided into m sub-iterations over which the correction vector is "dithered" according to Eq. (5.21) to ensure non-singular estimation.
  • the optimal adaptive control law was implemented for a two-plane active balancing system on a laboratory flexible rotor test rig.
  • the test rig consisted of a 0.65" (16,5mm) shaft supported over a span of approximately 30 inches (762 mm) on two ball bearings and driven through a narrow "quill-shaft" coupled to a direct current type motor.
  • Active balancing devices were mounted to the shaft at approximately the third-span locations.
  • Each balance actuator and disk assembly weighed approximately 10 lbs. (4.5 kg). Eddy current proximity probes mounted close to, and outboard of the two disk locations were used to measure radial shaft deflection in two orthogonal (i. e., x and y) directions.
  • the first two critical speeds of the rotating test rig were measured to be approximately 1100rpm and 4360rpm.
  • the predicted mode shapes for these two critical speeds are shown in Figure 25 .
  • Figure 27 shows the results of active balancing using one balance plane (drive-end) compared with results using two balance planes.
  • Figure 30 shows a comparison of optimal control results four control scenarios when the initial influence matrix estimate is erroneous.
  • the influence matrix estimate for the test was approximately 0.6 times the magnitude and rotated 70° from the actual matrix shown in Table 1.
  • the first plot of Figure 30(a) shows the unstable performance resulting from traditional non-adaptive control under this condition.
  • Figure 30 (b) shows how the non-adaptive control was stabilized using a non-zero [R] matrix.
  • Figure 30 (c) illustrates non-adaptive control stabilization using a non-zero [S] matrix.
  • Figure 30 (d) shows the results of adaptive control.
  • the stability criterion For stable control, the stability criterion must be below the value one. Note that the unstable traditional non-adaptive control has a stability criterion value of close to two. By adding nonzero penalty matrices [ R ] and [ S ], the stability criterion value drops below one and the control converges.
  • the stability criterion for the adaptive control begins at the same unstable value as the traditional non-adaptive control. This corresponds to the temporarily worse vibration in the first control iteration. However, as the on-line estimation converges to the actual influence matrix, the adaptive stability criterion value falls below one and eventually falls close to zero. The result is that the control converges rapidly.
  • the non-adaptive control is rendered stable using the non-zero [ S ] matrix. Furthermore, the performance in terms of weighted least-squares error is better than the case of using a non-zero [ R ] matrix. However, it is important to note that the steady-state performance using the non-zero [ S ] matrix was still sub-optimal because of the inaccurate influence matrix estimate. The minimum steady-state objective function value attained with the non-adaptive control was 0.16. Though much lower than the uncontrolled value, this was almost 2.5 times greater than the minimum objective function value of 0.0665 attained using the adaptive control. Thus it is clear that adaptive control not only provides fast and stable control convergence, but also provides more optimal steady-state performance as predicted by observing Eq. (5.15) and Eq. (5.16).

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Balance (AREA)
  • Automatic Control Of Machine Tools (AREA)
  • Machine Tool Units (AREA)
  • Auxiliary Devices For Machine Tools (AREA)
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US6618646B1 (en) 2003-09-09
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